Genotyping of RB1 status identifies two distinct subtypes in EGFR‐mutant lung cancers with SCLC transformation

In this study, we investigate the significant yet under-explored clinical and molecular heterogeneity in epidermal growth factor receptor ( EGFR)- mutant lung cancers transformed into small cell lung

Baseline demographics of these subgroups are shown in Table 1.It is well known that RB1 inactivation favours T-SCLC in EGFR-mutant tumours. 6Unexpectedly, the EGFR/RB1-mutant subgroup demonstrated a significantly longer median time to transformation than the EGFR-mutant/RB1-wild subgroup (Figure 1A).Patients with the T790M mutation or the SMAD4 mutation also showed a significantly prolonged time to transformation (Figure 1B,C; Supporting Information Table S1).However, multivariate analysis identified RB1 and SMAD4, not T790M status, as independent prognostic factors for transformation (Figure 1D; Supporting Information Table S2).Of note, the number of SMAD4-mutant patients was limited (n = 2).This suggests that some other mechanisms rather than RB1 mutation might drive T-SCLC in the EGFR-mutant/RB1-wild subgroup.Post-transformation survival and overall survival did not differ between the subgroups (Supporting Information Figure S2A-D).
Genomic analysis showed that despite no significant pre-transformation mutational differences between the subgroups, the EGFR/RB1-mutant subgroup, similar to de novo SCLC, had a higher frequency of copy number variations (CNVs) than the EGFR-mutant/RB1-wild subgroup (Figure 1E,F).In the analysis of matched pre-and posttransformation samples, PIK3CA alterations were prevalent post-transformation across both subgroups, while AKT1 and MYC mutations were specifically enriched in the EGFR/RB1-mutant subgroup (Figures 1G and S2E).Additionally, in EGFR/RB1/TP53-mutant patients, those in the transformed subgroup exhibited a higher mutation load, particularly in the phosphatidylinositol 3-kinase (PI3K)/AKT pathway, and demonstrated a more frequent CNVs≥ 2 than the untransformed subgroup (Figures 1H-J  and S2F).
Further transcriptomic analysis revealed that pretransformation genes and pathways associated with neuroepithelial cell differentiation and ion regulation were up-regulated in the EGFR/RB1-mutant subgroup, while immune-related genes and pathways were significantly suppressed, compared to the EGFR-mutant/RB1-wild subgroup, indicating a closer association with SCLC (Figure 2A-C).Further comparing paired pre-and post-transformation EGFR-mutant/RB1-wild samples, we observed a significant downregulation of tumour necrosis factor (TNF) signalling, hypoxia-inducible factor 1 (HIF-1) signalling and T-cell differentiation pathways, whereas neuroactive ligand-receptor interaction and stem cell regulation pathways were more activated post-transformation.Notably, PI3K/AKT signalling was inhibited, and no significant change was observed in the EGFR signalling pathway upon T-SCLC, suggesting a low dependence on EGFR and PI3K/AKT pathways in the EGFR-mutant/RB1-wild subgroup (Figure 2D; Supporting Information Table S3).
Next, we used single-cell RNA sequencing to analyze two post-transformation samples from the EGFRmutant/RB1-wild subgroup (Pt18) and the EGFR/RB1mutant subgroup (Pt32; Figure 2E).A total of 737 malignant cells were identified and classified into five distinct clusters (Supporting Information Figure S3).According to the expression of four classical transcriptional regulators, the cancer cells from Pt18 predominantly aligned with the ASCL1 subtype, characterised by Notch and HIF-1 signalling pathways, while the sample from Pt32 corresponded to the NEUROD1 subtype, marked by significant  Although NSCLC patients harbouring triple EGFR/RB1/TP53-mutations have an elevated risk of transformation to SCLC, only 18% of them actually undergo transformation. 7Through comparative analysis of clinical profiles and molecular signatures between the transformed and the untransformed subgroups, we identified independent risk factors for transformation, including age, EGFR mutation type, CNV events and mutations of the PI3K/AKT pathway (Supporting Information Table S4).Based on these, a nomogram model was developed to predict the SCLC-transformation risk (Figure 3A-D).The model's robustness was further vali-dated in an external cohort, including three transformed and five untransformed cases, demonstrating its certain reliability (Figure 3E).Currently, an active clinical trial is evaluating the efficacy of osimertinib and chemotherapy in delaying T-SCLC in EGFR/RB1/TP53-mutant patients (NCT03567642).This model might help to identify the beneficiaries of combination treatment.
To our knowledge, this is the first study to define subtypes of T-SCLC, offering important perspectives on the disparate pathogenesis and potential therapeutic targets.The EGFR-mutant/RB1-wild subgroup exhibited a more active immune microenvironment, which became markedly 'colder' after transformation, indicating that immunotherapy might be an appropriate therapy.Our previous retrospective study revealed that immunochemotherapy, with or without bevacizumab, might be a promising approach for treating T-SCLC. 8n contrast, the EGFR/RB1-mutant subgroup exhibited a marked increase in PI3K/AKT pathway mutations posttransformation, suggesting that PI3K/AKT pathway activation might be essential for transformation, consistent with previous findings. 9,10Consequently, this subgroup might be sensitive to AKT inhibitors, such as samotolisib.
In conclusion, our study comprehensively characterises T-SCLC patients, introducing a novel classification based on RB1 status (Figure 3F).Our results deepen the understanding of molecular biology underlying T-SCLC and pave the way for personalised treatment strategies.In addition, our predictive nomogram model significantly improves the accuracy of assessing the SCLCtransformation risk in EGFR/RB1/TP53-mutant NSCLCs, highlighting the need for further validation.

A U T H O R C O N T R I B U T I O N S
Conception and design: Junjian Wang, Yi-Long Wu and Jie Huang.Development of methodology: Jie Huang, Chao Zhang and Shi-Ling Zhang.Acquisition of data: Weiye Huang, Zhenhua Zhang, Yu-Qing Chen and Jun-Wei Su.Analysis and interpretation of data: Jie Huang, Hong-Hong Yan, Hua-Jun Chen and Jin-Ji Yang.Writing, review and/or revision of the manuscript: Junjian Wang, Yi-Long Wu, Jie Huang, Chao Zhang and Shi-Ling Zhang.Administrative, technical or material support: Hong-Hong Yan, Hua-Jun Chen and Jin-Ji Yang.Study supervision: Junjian Wang and Yi-Long Wu.All authors read and approved the final version of the manuscript.Jie Huang, Shi-Ling Zhang and Chao Zhang have accessed and verified the data.Junjian Wang and Yi-Long Wu were responsible for the decision to submit the manuscript.

A C K N O W L E D G E M E N T S
We thank Prof. Yongchang Zhang from Hunan Cancer Hospital and Prof. Jia Zhong from the National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital for providing external cohort data.We thank all the patients who participated in this study and their families.Besides, the authors thank Burning Rock Biotech (Guangzhou, China) for their valuable assistance in data analysis.

C O N F L I C T O F I N T E R E S T S TAT E M E N T
All the authors declare that they have no conflicts of interest.

E T H I C S S TAT E M E N T
This study was approved by the research ethics committee of Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, and carried out under the World Medical Association Declaration of Helsinki.

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I G U R E 1 Clinical and genomic comparison between subgroups of epidermal growth factor receptor (EGFR)-mutant lung cancer patients with small cell lung cancer (SCLC) transformation and EGFR/RB1/TP53-mutant non-SCLC (NSCLC) patients.(A) Time to transformation of the EGFR/RB1-mutant subgroup and the EGFR-mutant/RB1-wild subgroup.(B) Time to transformation of T790M-positive subgroup versus negative subgroup.(C) Time to transformation of SMAD4-wild subgroup versus mutant subgroup.(D) Forest plot for transformation-free survival.(E) Baseline genomic landscape of EGFR-mutant lung cancer with SCLC transformation (T-SCLC).(F) Comparison of the frequency of copy number variation (CNV) between the EGFR/RB1-mutant subgroup and the EGFR-mutant/RB1-wild subgroup.(G) Enrichment analysis of genomic alterations during transformation in the EGFR/RB1-mutant subgroup and the EGFR-mutant/RB1-wild subgroup.(H) The frequency of CNV (≥2/ < 2) in transformed and untransformed subgroups of EGFR/RB1/TP53-mutant NSCLC patients.(I) Overall mutations in transformed and untransformed subgroups.(J) Mutations of phosphatidylinositol 3-kinase (PI3K/AKT) pathway in transformed and untransformed subgroups.

F I G U R E 2
Transcriptomic analysis and the single-cell RNA sequencing analysis of EGFR-mutant lung cancer patients with T-SCLC.(A) A volcano plot of differentially expressed genes (DEGs) between EGFR-mutant/RB1-wild subgroup and EGFR/RB1-mutant subgroup.(B) Pathway enrichment analyses on the DEGs between the EGFR-mutant/RB1-wild subgroup and the EGFR/RB1-mutant subgroup.(C) Gene Ontology analysis of differentially enriched biological processes between the EGFR-mutant/RB1-wild subgroup and the EGFR/RB1-mutant subgroup.(D) Unsupervised clustering heatmap of DEGs and differentially regulated pathways after transformation in EGFR-mutant/RB1-wild lung cancers.(E) Uniform manifold approximation and projection (UMAP) plots of malignant cells colour-coded by patients.(F) Feature plots of ASCL1 and NEUROD1 RNA expression levels across all the clusters.(G) GSEA of differentially enriched pathways between the EGFR-mutant/RB1-wild sample and the EGFR/RB1-mutant sample.Pt32, patient 32 from the EGFR/RB1-mutant subgroup; Pt18, patient 18 from the EGFR-mutant/RB1-wild subgroup.F I G U R E 3 A prediction nomogram model in EGFR/RB1/TP53 lung cancer patients.(A) Nomogram to predict the risk of transformation.(B) Receiver operating characteristic (ROC) curves of the nomogram in the training cohort.(C) Calibration plots of the nomogram.(D) Decision curve analysis (DCA) for the nomogram.(E) ROC curves of the nomogram in the external validation cohort.(F) The graphic abstract of the study, including cohorts collection, methods and findings.upregulation of oxidative phosphorylation and cell cycle pathways (Figure 2F,G).

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U N D I N G I N F O R M AT I O N The High-level Hospital Construction Project (Grant Number DFJH201917); Guangdong Association of Clin-ical Trials (GACT)/Chinese Thoracic Oncology Group (CTONG) and Guangdong Provincial Key Lab of Translational Medicine in Lung Cancer (Grant Number 2017B030314120); Guangdong Basic and Applied Basic Research Foundation (Grant Number 2019B151502016, 2022B1515130008) D ATA AVA I L A B I L I T Y S TAT E M E N T Sequencing data and code for this study can be obtained by contacting the corresponding author upon reasonable request.